Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Twitter Sentiment Analysis Based on Neural Network Techniques Subject The topic of the resource Comparison of various classification techniques; Convolutional neural network (CNN); Decision tree; Multilayer perceptron (MLP); Nae Bayes; Recurrent neural network (RNN); SVM; Twitter sentiment analysis Description An account of the resource Our whole world is changing everyday due to the present pace of innovation. One such innovation was the Internet which has become a vital part of our lives and is being utilized everywhere. With the increasing demand to connected and relevant, we can see a rapid increase in the number of different social networking sites, where people shape and voice their opinions regarding daily issues. Aggregating and analysing these opinions regarding buying products and services, news, and so on are vital for todays businesses. Sentiment analysis otherwise called opinion mining is the task to detect the sentiment behind an opinion. Today, analysing the sentiment of different topics like products, services, movies, daily social issues has become very important for businesses as it helps them understand their users. Twitter is the most popular microblogging platform where users put voice to their opinions. Sentiment analysis of Twitter data is a field that has gained a lot of interest over the past decade. This requires breaking up tweets to detect the sentiment of the user. This paper delves into various classification techniques to analyse Twitter data and get their sentiments. Here, different features like unigrams and bigrams are also extracted to compare the accuracies of the techniques. Additionally, different features are represented in dense and sparse vector representation where sparse vector representation is divided into presence and frequency feature type which are also used to do the same. This paper compares the accuracies of Nae Bayes, decision tree, SVM, multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN), and their validation accuracies ranging from 67.88 to 84.06 for different classification techniques and neural network techniques. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Singal A.; Thiruthuvanathan M.M. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 33-48. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-9416-5_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-9416-5_3</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133695429&amp;doi=10.1007%2F978-981-16-9416-5_3&amp;partnerID=40&amp;md5=65716e980f1bcc4391fd908037e65559" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133695429&amp;doi=10.1007%2f978-981-16-9416-5_3&amp;partnerID=40&amp;md5=65716e980f1bcc4391fd908037e65559</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singal A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Bangalore, 560074, India; Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Bangalore, 560074, India